Many different approaches for segmenting pixels in an image have been proposed. Among the common pixel segmentation approaches are thresholding and region growing.
Thresholding involves classifying pixels based on their respective grayscale or color values, where pixels with values below a threshold are classified into a first group and pixels with values above the threshold are classified into a second group. In some thresholding approaches a single, global threshold is used to segment pixels into the first and second groups. In other, so-called “adaptive thresholding” approaches, local thresholds are computed based on the characteristics of respective sub-regions of an image and the computed local thresholds are used to classify pixels in the corresponding image sub-regions.
Region growing is an image segmentation process that merges pixels into regions based on predefined criteria. Region growing processes typically begin with the identification of one or more “seed” regions each containing one or more seed pixels. Pixels that have characteristics similar to adjacent seed regions are merged into the adjacent seed regions in accordance with the predefined criteria. In one region growing approach, pixels are merged into adjacent seed regions when certain features of the pixels, such as color or texture, are similar enough to the adjacent seed regions to satisfy a prescribed local or global similarity constraint. The similarity constraint typically is implemented by computing measures of distances between pixels and the seed regions, and comparing the distance measures to corresponding thresholds. The similarity constraint implementation may involve the use of a single global threshold that is used to merge pixels into the seed regions. Alternatively, the similarity constraint implementation may involve the use of adaptive thresholds that are computed for particular sub-regions of a given image based on the characteristics of the sub-regions.
In general, noise reduces the accuracy with which pixels may be segmented. For example, in some thresholding approaches, a low threshold may result in a noisy background pixel being misclassified as a foreground pixel and a high threshold may result in a noisy foreground pixel being misclassified as a background pixel. The segmentation accuracy of previously proposed thresholding and region growing pixel segmentation approaches typically is insufficient for demanding applications, such as object extraction for commercial print advertising, especially in regions where the contrast between the object and the background is low.